Methods and Systems for Dynamically Rearranging Search Results into Hierarchically Organized Concept Clusters

ABSTRACT

Methods of and systems for dynamically rearranging search results into hierarchically organized concept clusters are provided. A method of searching for and presenting content items as an arrangement of conceptual clusters to facilitate further search and navigation on a display-constrained device includes providing a set of content items and receiving incremental input to incrementally identify search terms for content items. Content items are selected and grouped into sets based on how the incremental input matches various metadata associated with the content items. The selected content items are grouped into explicit conceptual clusters and user-implied conceptual clusters based on metadata in common to the selected content items. The clustered content items are presented according to the conceptual clusters into which they are grouped.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 119(e) to thefollowing application, the contents of which are incorporated byreference herein:

-   -   U.S. Provisional Patent Application No. 60/825,616, entitled A        Method Of Dynamically Generating Hierarchically Organized Result        Clusters For An Incremental Search Query Matching One Or More        Precomputed Hierarchical Clusters, filed on Sep. 14, 2006.

This application is related to the following applications, the contentsof which are incorporated by reference herein:

-   -   U.S. patent application Ser. No. 11/136,261, entitled Method And        System For Performing Searches For Television Programming Using        Reduced Text Input, filed on May 24, 2005, which claims priority        to U.S. Provisional Patent Application No. 60/626,274, entitled        Television Systems and Associated Methods, filed on Nov. 9,        2004, and U.S. Provisional Patent Application No. 60/664,879,        entitled Method And System For Performing Searches For        Television Programming Using Reduced Text Input, filed on Mar.        24, 2005;    -   U.S. patent application Ser. No. 11/312,908, entitled Method And        System For Dynamically Processing Ambiguous, Reduced Text Search        Queries And Highlighting Results Thereof, filed on Dec. 20,        2005, which claims priority to U.S. Provisional Patent        Application No. 60/711,866, entitled A Dynamic Highlighting        Interface Of Multiword Prefixes Of Results Obtained By        Incremental Search With Reduced Text Entry On Television And        Mobile Devices Using A Keypad With Overloaded Keys, filed on        Aug. 26, 2005, and U.S. Provisional Patent Application No.        60/716,101, entitled Method And System For Incremental Search        With Reduced Text Entry Using A Reduced Keypad With Overloaded        Keys, filed Sep. 12, 2005; and    -   U.S. patent application Ser. No. 11/235,928, entitled Method And        System For Processing Ambiguous, Multiterm Search Queries, filed        on Sep. 27, 2005, which claims priority to U.S. Provisional        Patent Application No. 60/716,101, entitled Method And System        For Incremental Search With Reduced Text Entry Using A Reduced        Keypad With Overloaded Keys, filed Sep. 12, 2005, and U.S.        Provisional Patent Application No. 60/711,866, entitled A        Dynamic Highlighting Interface Of Multiword Prefixes Of Results        Obtained By Incremental Search With Reduced Text Entry On        Television And Mobile Devices Using A Keypad With Overloaded        Keys, filed on Aug. 26, 2005.

BACKGROUND

1. Field of Invention

The present invention relates to a method of selecting and presentingcontent and, more specifically, to a method of dynamically combining andorganizing content into hierarchical clusters to facilitate userdiscovery of desired information.

2. Description of Related Art

One measure of the usability of an information finding and presentationsystem on input and/or display constrained devices is the effortexpended by the user in the discovery of desired information (thediscovery of information could be text based search, browsing a contentspace, or some combination of both). One method of minimizing the effortexpended to find information (either via search or browse techniques) oninput and display constrained devices is the use of incremental searchtechniques. The use of incremental search, where results are retrievedas user types in each character, is far superior to full word searchinterfaces on input constrained device, because incremental searchreduces the amount of text the user must input (See, for example, thetechniques presented in the applications incorporated below).

However, one of the challenges in an incremental search system is topresent the most relevant results to the user even when the input issparse or is of an ambiguous nature, such as input using an overloadedkeypad with multiple alphanumeric characters mapped to the same physicalkey. For example, a pure lexical match on incremental input would failto yield good results where exact matches on prefixes are rated as morerelevant than partial word matches. Furthermore, if the input method isusing an overloaded keypad, generating an ambiguous text input, then theproblem is even worse.

In addition, ambiguous text inputs can match a wide variety of resultsbecause of the nature of the ambiguous input. This is so because theambiguous input not only represents the search input intended by theuser, but can also represent other words or phrases. For example, usingthe well-known 12-key telephone keypad, the input “227” represents both“car” and “bar”, which can match very different results. Thus, whileincremental, ambiguous text input is a convenient way to enter searchinput on an input constrained device, the increase in the amount ofresults returned can be cumbersome on a display constrained device,where only a few entries in a result set are visible.

SUMMARY OF THE INVENTION

The invention provides a method of dynamically rearranging searchresults for an incremental search query into hierarchically organizedconcept clusters.

Under one aspect of the invention, a method of searching for andpresenting content items as an arrangement of conceptual clusters tofacilitate further search and navigation on a display-constrained deviceincludes providing a relatively large set of content items. At leastsome of the content items have metadata to specify explicit conceptsassociated with the content items. At least some of the metadata includephrases having more than one metadata term. The method further includesreceiving from a user incremental input to incrementally identify morethan one search term for desired content items and selecting from therelatively large set of content items: a first set of content items,wherein all search terms match metadata terms of a single one of themetadata phrases of each content item of said first set, a second set ofcontent items, wherein a first subset of the search terms matches atleast one metadata term of at least a first metadata phrase of eachcontent item of said second set, and a third set of content items,wherein a second subset of the search terms matches at least onemetadata term of at least a second metadata phrase of each content itemof said third set, the first metadata phrase differing from the secondmetadata phrase. The method also includes grouping the content items thesecond and third sets have in common to form an intersection set foruser-implied concepts inferred from the explicit concepts associatedwith the metadata of the content items of the intersection set andorganizing the content items of the first set and the intersection setinto conceptual cluster sets. The content items of the first set areorganized into explicit conceptual cluster sets based on the metadataphrases having metadata terms matching the search terms so that contentitems having a same metadata phrase matching the search terms areclustered together. The content items of the intersection set areorganized into user-implied conceptual clusters based on at least thefirst and second metadata phrases the content items of the intersectionset have in common so that content items having same first and secondmetadata phrases matching the search terms are clustered together. Themethod includes presenting the content items organized into the explicitconceptual cluster sets and the user-implied conceptual cluster sets.Each explicit conceptual cluster set is identified based on the metadataphrase common to the content items of said explicit conceptual clusterset having metadata terms matching the search terms. Each user-impliedconceptual cluster set is identified based on the first and secondmetadata phases the content items of said user-implied conceptualcluster set have in common.

Under another aspect of the invention, the incremental input isambiguous text input; the ambiguous text input has one or more digits;and each digit represents more than one alphanumeric character.

Under a further aspect of the invention, the method further comprisesmodifying the metadata terms of at least one of the metadata phrases ofat least some of the content items based on at least one of the date,day, and time of the incremental input.

Under yet another aspect of the invention, the presenting the contentitems is on a display-constrained device.

Under yet a further aspect of the invention, the incremental inputcomprises at least two prefixes in an ordered format and/or at least twoprefixes in an unordered format. The incremental input can comprise atleast two prefixes separated by a word separator.

Under an aspect of the invention, the organized content items areordered for presentation in accordance with a given relevance function.The relevance function comprises at least one of temporal relevance ofthe content items, location relevance of the content items, popularityof the content items, and preferences of the user.

Under another aspect of the invention, at least some of the metadataterms include phonetically equivalent terms to the explicit conceptsassociated with at least some of the content items and/or commonlymisspelled terms of the terms of the metadata phrases.

Under yet another aspect of the invention, the method further comprisesorganizing the content items of the large set of content items into apredetermined hierarchy based on a relationship between theinformational content of the content items. The metadata to specify theexplicit concepts associated with the content items is selected based onthe predetermined hierarchy.

Under an aspect of the invention, a system for searching for andpresenting content items as an arrangement of conceptual clusters tofacilitate further search and navigation on a display-constrained deviceincludes a database stored in an electronically readable medium forcataloging a relatively large set of content items. At least some of thecontent items have metadata to specify explicit concepts associated withthe content items. At least some of the metadata include phrases havingmore than one metadata term. The system also includes input logic forreceiving from a user incremental input to incrementally identify morethan one search term for desired content items and selection logic forselecting from the relatively large set of content items a first set ofcontent items, wherein all search terms match metadata terms of a singleone of the metadata phrases of each content item of said first set, asecond set of content items, wherein a first subset of the search termsmatches at least one metadata term of at least a first metadata phraseof each content item of said second set, and a third set of contentitems, wherein a second subset of the search terms matches at least onemetadata term of at least a second metadata phrase of each content itemof said third set, the first metadata phrase differing from the secondmetadata phrase. The system further includes grouping logic for groupingthe content items the second and third sets have in common to form anintersection set for user-implied concepts inferred from the explicitconcepts associated with the metadata of the content items of theintersection set and organization logic for organizing the content itemsof the first set and the intersection set into conceptual cluster sets.The content items of the first set are organized by the logic intoexplicit conceptual cluster sets based on the metadata phrases havingmetadata terms matching the search terms so that content items having asame metadata phrase matching the search terms are clustered together.The content items of the intersection set are organized by the logicinto user-implied conceptual clusters based on at least the first andsecond metadata phrases the content items of the intersection set havein common so that content items having same first and second metadataphrases matching the search terms are clustered together. The systemalso includes presentation logic for presenting the content itemsorganized into the explicit conceptual cluster sets and the user-impliedconceptual cluster sets. Each explicit conceptual cluster set isidentified based on the metadata phrase common to the content items ofsaid explicit conceptual cluster set having metadata terms matching thesearch terms. Each user-implied conceptual cluster set is identifiedbased on the first and second metadata phases the content items of saiduser-implied conceptual cluster set have in common.

Under another aspect of the invention, at least a portion of thedatabase stored in an electronically readable medium is implemented in aserver system remote from the user.

Under yet another aspect of the invention, at least one of the inputlogic, the selection logic, the grouping logic, the organization logic,and the presentation logic is implemented in a server system remote fromthe user.

Under a further aspect of the invention, the incremental input isambiguous text input. The ambiguous text input has one or more digits.Each digit represents more than one alphanumeric character.

Under yet a further aspect of the invention, the system also includesmodification logic for modifying the metadata terms of at least one ofthe metadata phrases of at least some of the content items based on atleast one of the date, day, and time of the incremental input.

Under another aspect of the invention, the system also includes rankinglogic for ordering the organized content items for presentation inaccordance with a given relevance function. The relevance function caninclude at least one of temporal relevance of the content items,location relevance of the content items, popularity of the contentitems, and preferences of the user.

These and other features will become readily apparent from the followingdetailed description where embodiments of the invention are shown anddescribed by way of illustration.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

For a more complete understanding of various embodiments of the presentinvention, reference is now made to the following descriptions taken inconnection with the accompanying drawings in which:

FIG. 1 illustrates a method of organizing content items and conceptsinto hierarchical time-sensitive concept clusters, matching incrementaluser input with one or more concept clusters, and generating andpresenting relevant dynamic hierarchical clusters to the user.

FIG. 2 illustrates a concept cluster hierarchy.

FIG. 3 illustrates different concept cluster hierarchies associated withdifferent results.

FIG. 4 illustrates an embodiment of the invention where search resultsfor a partial prefix input are returned, including lexical matches,predetermined concept clusters, and dynamically generated conceptclusters.

FIG. 5 illustrates the user's discovery of information, by expanding aconcept cluster.

FIG. 6 illustrates the user's discovery of information, by expanding aconcept cluster.

FIG. 7 illustrates the user's discovery of information, where a dynamicconcept cluster is created, based on the partial prefix input entered bythe user, and then the dynamic concept cluster is expanded by the user.

FIG. 8 illustrates a concept cluster hierarchy and the user's discoveryof information, by conflating concept clusters.

FIG. 9 illustrates a content system for the selection, reorganization,and presentation of content items.

FIG. 10 illustrates a user device for selecting, reorganizing, andpresenting selected content items.

DETAILED DESCRIPTION

Preferred embodiments of the invention provide methods of and systemsfor discovering and dynamically rearranging search results intohierarchically organized concept clusters. A concept cluster is a set ofcontent items and/or topics that are related by one or more commonthemes or information types. For example, one concept cluster may be“baseball”, which can contain search results related to scores of pastMajor League Baseball games and/or schedules for future games. In someimplementations, the concept clusters are time-sensitive (describedbelow) and include both precomputed concept clusters and dynamicallygenerated concept clusters. The search results can include lexicalmatches between the content results and the incremental input of searchqueries, as well as matches between the incremental input and theconcept cluster identifiers. This method of generating and presentingsearch results significantly enhances the user experience of performingincremental search for information because the hierarchicalconcept-driven clustering of results provides a richer organization ofresults. The techniques disclosed herein enable the user to more easilyfind the desired information content, as all results pertaining to aparticular concept have been collected together. This stands in contrastto lexical matching, where results pertaining to the same concept may beinterleaved among other results, which increases the cognitive load forthe user.

Embodiments of the present invention build on techniques, systems andmethods disclosed in earlier filed applications, including but notlimited to U.S. patent application Ser. No. 11/204,546, entitled Methodand System For Performing Searches For Television Content and ChannelsUsing a Non-intrusive Television Interface and With Reduced Text Input,filed on Aug. 15, 2005; U.S. patent application Ser. No. 11/246,432,entitled Method And System For Incremental Search With Reduced TextEntry Where The Relevance Of Results Is A Dynamically Computed Functionof User Input Search String Character Count, filed on Oct. 7, 2005; U.S.patent application Ser. No. 11/509,909, entitled User Interface ForVisual Cooperation Between Text Input And Display Device, filed Aug. 25,2006; U.S. patent application Ser. No. 11/561,197, entitled Method AndSystem For Finding Desired Results By Incremental Search Using AnAmbiguous Keypad With The Input Containing Orthographic and TypographicErrors, filed Nov. 17, 2006; and U.S. patent application Ser. No.11/682,693, entitled Methods and Systems For Selecting and PresentingContent Based On Learned Periodicity Of User Content Selection, filed onMar. 6, 2007, the contents of each of which are herein incorporated byreference. Those applications taught specific ways to performincremental searches using ambiguous text input, methods of ordering thesearch results, and techniques for learning a user's behavior andpreferences. The techniques disclosed in those applications can be usedwith the user's navigation behavior or the user's relationship to aconcept cluster described herein in the same or similar ways in whichthe techniques are applied to the collections of content items describedin those applications. The present techniques, however, are not limitedto systems and methods disclosed in the incorporated patentapplications. Thus, while reference to such systems and applications maybe helpful, it is not believed necessary to understand the presentembodiments or inventions.

FIG. 1 is a flowchart illustrating the operation of an embodiment of theinvention. The flowchart illustrates a method of searching for contentbased on the user's incremental search input and reorganizing andpresenting the results in hierarchically arranged concept clusters thatare dynamically created based on the content item results returned fromthe search. Content items are associated with metadata thatcharacterizes the content items. This can be done in a number of ways,including organizing the content items into a hierarchy thatcharacterizes the content items and describes the informationrelationship between the content items and concepts related to thecontent items. In such an embodiment, content items and concept clustersare first organized into a hierarchy that best represents therelationship between concept clusters and particular content items aswell as the relationship between the concept clusters themselves (step101). Because the content is organized into clusters of the hierarchy,each concept cluster can be a parent, child, or sibling cluster relativeto the other clusters in the hierarchy. Similarly, each content item canbe a member of one or more concept clusters. The organization of contentitems into concept clusters can be performed in a precomputation stepthat occurs on a routine basis before the user enters the search input,or the organization step can be triggered by, and occur immediatelybefore, processing the user's search input, described in more detailbelow.

As mentioned above, in some embodiments, this step can be omitted, asthe content items can be maintained without a hierarchy, and laterorganized according to metadata associated with the content items, asdescribed in greater detail below. Thus, in some implementations, thecontent items are simply associated with metadata and need not bearranged in a hierarchy. In such an embodiment, the content items have a“flat” arrangement in that there is no express hierarchy to the contentitem collection. The metadata associated with the content items consistsof metadata phrases that can have one or more terms to describe theinformational content of the content item.

The next step of the method calls for receiving search input from theuser (step 102). As explained above, the search input can be incrementaland ambiguous text input, entered using techniques disclosed in theincorporated applications. The search could also be based on browsing aninformation tree of the content. In an implementation utilizingambiguous text input, the systems and/or devices employing the methodsdisclosed herein can provide for an express word separator character,i.e., a character that unambiguously identifies that one ambiguoussearch term has ended and another has begun. By providing an expressword separator, the number of unambiguous search terms that can matchthe ambiguous input is reduced. Whereas, if an ambiguous character isused to represent a word separator, a text entry intended by the user tobe a multiple term entry can be interpreted by a disambiguation systemto be a single search term, thereby causing the search system to returnresults not of interest to the user. In addition, because the number ofpossible unambiguous search terms matching the ambiguous input isincreased, the processing load on the system is increased, which canresult in reduced system performance.

Content items are selected based on the user input (step 103). Thecontent search methods in the incorporated applications is useful forthis step. In one implementation, each content item is associated withone or more descriptive metadata terms. This metadata describes, forexample, the types of content items, the information contained in thecontent items, and keywords associated with the content items. Thus, theincremental input can be compared against the various descriptiveterms/metadata to identify content that matches what the user seeks.

The search input is then matched with concept clusters defined in step101 and/or metadata associated with the content items (step 104). Thematch can be based on a lexical match between the user's input and oneor more identifiers of the concept cluster and/or the metadataassociated with the content items, for example, by using the matchingand search techniques in the applications incorporated above. When ahierarchy is provided, the relative organization of the concept clusterhierarchy governs the presentation of the content items because thehierarchy determines, in part, what metadata is associated with thecontent items. Having identified content items, concept clusters, andmetadata that match the user's input, the method determines the besthierarchical organization of the selected content items for presentationto the user to aid in the user's selection or navigation of the selectedcontent items (step 105).

One method of hierarchically organizing the selected content items is togroup the content items into explicit conceptual clusters anduser-implied conceptual clusters. Explicit conceptual clusters aregroups of content items that have metadata phrases with terms that matchmultiple terms of the user's search input. Thus, it can be said thatthat concept expressed by the user's input match a concept that is foundexplicitly in a single metadata phrase. User-implied conceptual clustersare groups of content items are related by a concept that can beinferred from the user's search input. Thus, rather than the conceptbeing found within a single metadata phrase, the concept is formed bythe coming-together of multiple metadata phrases. Thus, content itemsthat have a first metadata phrase that matches a first portion of theuser's search input and a second metadata phrase that matches a secondportion of the user's search input are grouped into user-impliedconceptual clusters. Explicit conceptual clusters and user-impliedconceptual clusters are illustrated in the examples provided below.Finally, the method calls for reorganizing the selected content itemsaccording to the hierarchy, e.g. the conceptual clusters, determined instep 105 and presenting the selected content items in the hierarchy(step 106).

FIG. 2 is an example of an organization of information into hierarchicaltime-sensitive clusters (generated by step 101 of FIG. 1). FIG. 2illustrates the organization of information and data relating toentertainers 201. The entertainers cluster is further divided intoactors 202 and singers 203. Further still, personalities Tom Cruise 204and Jack Nicholson 205 are grouped under the actors cluster 202, whileTom Jones 206 is grouped under the singers cluster 203. Note, theentertainers cluster may be a child cluster of an upper-level parentcluster; it may have sibling clusters related to other personalities;and it may have additional child clusters 207.

The Tom Cruise cluster 204 has child clusters; one such cluster would bea cluster containing all TV content 208 in which Tom Cruise appears.Another meaningful concept cluster would be a cluster of web videos 209relating to Tom Cruise. Yet another cluster is movies 210 in which TomCruise appears. Further clusters 211 can be included in the informationhierarchy. These clusters 208-211 are generated based on metadataassociated with Tom Cruise. Because Tom Cruise is an actor, there is awide variety of audio/video content associated with this cluster. Thus,for these audio/video content items, Tom Cruise may be a metadata phase.The Jack Nicholson cluster 205 contains child clusters similar to theTom Cruise cluster 204 because both are actors. Further actors can beassigned to addition clusters 212. The information in these clusters issaid to be time-sensitive because the information contained in theclusters or sub-clusters can change according to the time of day ordate. For example, TV shows can begin playing at a certain time of dayon a particular date. The organization of data can be done during theprecomputation step described above, and the results are subsequentlyused when user performs an incremental search.

The Tom Jones cluster 206 also has child clusters, but because Tom Jonesis a singer, the child clusters under the Tom Jones cluster 206 differfrom those generated for the actor clusters. For example, a CDs cluster213 containing Tom Jones music CDs available for sale, and a concertscluster 214 listing known Tom Jones concert dates and information arefound under the Tom Jones cluster 206. Thus, Tom Jones is a metadataphase associated with a concert content item. Further child clusters 215can be included. Likewise, additional personality clusters 216 can befound under the singers cluster 203.

As mentioned above, the concept clusters can be created based on themetadata associated with the content items. However, not every metadataterm may be selected to also serve as a concept cluster. For example, inone implementation, terms that occur among the metadata of the entireset of content items are used to create the concept cluster hierarchy.In a further example, the concept clusters are created based on popularcategorizations of the content items. Thus, one concept cluster would be“sports”, which would have sub-clusters “baseball”, “basketball”, etc.Another set of clusters would be “movies”, which would have subsclusters“genres”, “actors”, “directors”, etc. Any meaningful organization ofconcept clusters can be used with the techniques disclosed herein, andthe invention is not limited to any particular method of generating theclusters and the corresponding hierarchy.

FIG. 3 provides an example of the reorganization and presentation ofsearch results. A user enters “Tom” 301 as a prefix for “Tom Cruise”into a system supporting incremental search. The prefix “Tom” is matchedwith concept clusters such as “TV content”, “web videos”, and “movies”by way of these clusters' relationship with the parent cluster node “TomCruise” 302. Thus, in this example, Tom Cruise is an explicit conceptualcluster. However, rather than presenting the TV content, web videos, andmovies of Tom Cruise under a single cluster “Tom Cruise”, the systemdynamically creates the “Tom Cruise . . . TV Content”, “Tom Cruise . . .Web Videos”, and “Tom Cruise . . . Movies” clusters, effectively“flattening” a portion of the cluster hierarchy associated with TomCruise. This facilitates the user's selection and navigation of theresults related to Tom Cruise by displaying the variety of Tom Cruisecontent on one screen.

The input also matches other concept clusters associated with the term“Tom”, such as content related to “Tom Jones” 303, again, anotherexample of an explicit conceptual cluster. Because Tom Jones is asinger, there are different concept sub-clusters associated with theparent cluster of “Tom Jones”, for example, CDs of his music, concertdates, etc. As above, the system dynamically flattens a portion of theTom Jones cluster hierarchy to achieve the benefits described above. Thedecision of whether to flatten or not flatten portions of the predefinedhierarchy can be based on the number of items that would result in thelist of results to be presented. The ideal number of results can bedetermined based on the type of device on which the techniques areemployed and user preferences.

Meanwhile, the system discovers content items based on the matchingtechniques described in the incorporated applications and/or lexicalmatches of the content items' metadata with the search input “Tom”.These search results are then presented in the concept cluster hierarchydetermined according to the concept cluster match and reorganizationdescribed above. Thus, all content related to Tom Cruise is organizedaccording to the sub-clusters that are child nodes under Tom Cruise; allcontent related to Tom Jones is organized in a similar manner under thesub-clusters associated with Tom Jones.

FIG. 4 illustrates employment of the techniques disclosed herein toreorganize search results from a partial prefix search input. Thehierarchical reorganization in FIG. 4 is generated by performing lexicalmatches of the search input 401 against the content items andprecomputed concept clusters (e.g., the clusters of FIG. 2) anddynamically generating new concept clusters 402 based on the matchingresults. The user incrementally inputs partial prefixes of two castmembers 401. In this example, “Tom” for Tom Cruise and “Jac” for JackNicholson. The incremental input matches content items from a relativelylarge set of content items, some of which are arranged into new conceptclusters 402 that are dynamically-formed (e.g., the user-impliedconceptual clusters), while others are presented directly in the resultspresentation 403. In both cases, the partial prefix inputs 401 arematched against the results and the results are order by relevance (seethe incorporated applications for methods of ordering by relevance).

Dynamically-created concept clusters 402 can be formed by creating a newcluster that will contain sub-clusters and content items that satisfyboth prefixes of the search criteria, i.e., “Tom” and “Jac”. This aspectwill be described in greater detail below. One method of naming thedynamically-created concept clusters 402 is to combine the differentclusters that came together to form the new cluster. For example,dynamically-formed concept clusters 402 that are presented to the userinclude “Tom Cruise . . . Jack Nicholson,” “Tom Wilkinson . . . JackieChan,” “Tom Jones . . . Jack Nicholson,” and “Marisa Tomei . . . JackNicholson”, where each person's name represents a cluster associatedwith that person. Thus, each of clusters 402 is an example of auser-implied conceptual cluster, in that, no single metadata phraseassociated with a content item contains both personalities. Theuser-implied conceptual cluster is formed based on a combination of twoseparate metadata phrases common to multiple content items of thecluster. An arrow symbol 404 associated with the various resultsindicate that additional child cluster nodes and/or content items areorganized beneath the result presented.

Results 403 are directly presented, i.e., are not grouped into conceptclusters, and include “The Cat From Outer Space,” a movie with TomJackman, “Nothing in Common,” a movie with Jackie Gleason and Tom Hanks,“The Pledge,” a movie with Jack Nicholson and Tom Noonan, and“Sliders:Eggheads” a TV show with Tom Jackson. These results 403 are notorganized into dynamic concept clusters because (1) the content itemcontains metadata matching both partial prefix terms (i.e., an explicitconceptual cluster) and/or (2) only one result is found having thespecific terms which caused the content item result to be presented. Forexample, “The Cat From Outer Space” appears as a match because bothsearch terms, “Tom” and “Jac” appeared in the metadata “Tom Jackman”associated with that movie. Whereas the result “The Pledge” appears as amatch because the first term “Tom” matches the metadata item “TomNoonan” associated with the movie “The Pledge” and the second term “Jac”matches a separate metadata item “Jack Nicholson” associated with thesame movie. However, in this example, no other content items areassociated with both metadata terms “Tom Noonan” and “Jack Nicholson”.Had other content items been discovered that also shared those twometadata, a “Tom Noonan . . . Jack Nicholson” dynamic cluster would havebeen created. This cluster would have contained the content item “ThePledge” as well as the other content items associated with both of thesemetadata terms. An arrow symbol 405 shown next to the result “Nothing inCommon” indicates that that result has child nodes, such as video clips,commentaries, and/or links to vendors that sell a DVD of the movie.

One distinction of the techniques disclosed herein over other searchand/or presentation methods is the non-lexical nature of conceptclusters. The combination of Tom Cruise and Jack Nicholson can itselfform a concept cluster. With such a concept match, the user is presentedwith a single result for “Tom Cruise . . . Jack Nicholson”. This resultcan be hierarchical and contain result items, such as particular movieswith both actors, and/or sub-clusters, such as lists of movies, lists ofTV shows, and/or links to other content with both actors. This dynamicaggregation of results into concept clusters greatly enhances the userexperience in contrast to other incremental search systems, where thematch is purely lexical in nature. For example, a purely lexical-basedsearch might return results with multiple items matching Tom Cruise andJack Nicholson where the results of intersecting the sets of contentitems associated with these two persons may be mixed within otherresults from other lexical matches, e.g., Tom Wilkinson and Jackie Chan.Furthermore, the ordering of the mixed results may be cumbersome due tothe different popularities of the individual results of thisintersection.

FIG. 5 illustrates the user's discovery of information, by expanding aconcept cluster. In this example, the user has incrementally entered“RE” as a search term 501. The user can continue to type more text tofurther refine the search or navigate into one of the results returnedfrom the incremental search. Here the concept cluster “Red Sox” 502 isone of the results currently matching the incremental text input “RE”501. If the user navigates 503 into the “Red Sox” concept cluster (anexplicit conceptual cluster), the sub-clusters within the hierarchy aredisplayed 504. These sub-clusters include the sub-clusters “Red Sox livegames,” “Red Sox TV schedule,” “Red Sox past games,” and “Red Sox webvideos”, which, in one implementation, contains only content itemsassociated with the Red Sox in some way. The “Red Sox live games,” “RedSox TV schedule,” and “Red Sox past games” sub-clusters aretime-sensitive clusters 505, whose contents are dynamically adjustedwith time. The “Red Sox web videos” sub-cluster is not time sensitiveand does not need to be dynamically adjusted with time. A content item“Blue Jays @ Red Sox” 506 is also presented among the results.

FIG. 6 illustrates the user's discovery of information, by expanding aconcept cluster. In this example, the user has incrementally entered“YAN” as a search term 601. As with the previous example, the user cancontinue to type more text to further refine the search or navigate intoone of the results returned from the incremental search. Here theconcept cluster “New York Yankees” 602 is one of the results currentlymatching the incremental text input “YAN” 601. If the user navigates 603into the “New York Yankees” concept cluster (an explicit conceptualcluster), the sub-clusters within the hierarchy are displayed 604. Thesesub-clusters include the sub-clusters “New York Yankees live games,”“New York Yankees TV schedule,” “New York Yankees past games,” “New YorkYankees web videos,” and “Baseball web videos.” Note, that in thisexample, in addition to content items associated with the New YorkYankees in some way, the list includes an item associated with a relatedconcept, namely, “Baseball web videos” 606, which is associated with themore general concept “baseball”. The “New York Yankees live games,” “NewYork Yankees TV schedule,” and “New York Yankees past games”sub-clusters are time-sensitive clusters 605, whose contents aredynamically adjusted with time. The “New York Yankees web videos” and“Baseball web videos” sub-clusters are not time sensitive and do notneed to be dynamically adjusted with time. A content item “Yankees @Royals” 607 is also presented.

FIG. 7 illustrates the presentation output by one implementation of theembodiment, where the information reorganization of a dynamic conceptcluster is based on the cluster hierarchy associated with clusters thatare common to matches of multiple terms in the user's incrementalpartial prefix input. In this example, the user has incrementallyentered “RE YAN” as a search input 701. Again, the user can continue totype more text to further refine the search or navigate into one of theresults returned from the incremental search. In response to the input,the concept cluster “Red Sox . . . New York Yankees” 702 is one of theresults currently matching the incremental text input “RE YAN” 701. The“Red Sox . . . New York Yankees” cluster 702 is dynamically created byintersecting the two concepts “Red Sox” and “New York Yankees” (thus,forming a user-implied conceptual cluster). During the pre-computationstep (step 101 of FIG. 1), the concept “Red Sox” was related to theconcept “baseball”, as was the concept “New York Yankees.”

Because both the concept “Red Sox” and the concept “New York Yankees”are related to the concept “baseball”, the dynamic, user-implied,concept cluster “Red Sox . . . New York Yankees” 702 is created andcontent associated with matches of the two input terms, “RE” and “YAN”,are organized according to the hierarchy of the shared parent concept“baseball” and presented to the user. Similar to previous examples, ifthe user selects the “Red Sox . . . New York Yankees” concept cluster702, the sub-clusters from the intersection of the two concepts aredisplayed 704. In this case, the dynamically-formed intersectionclusters are “Live Games,” “TV schedule,” “web videos,” and “pastgames.” Again, this organization is governed by the informationhierarchy associated with the parent concept “baseball”, which can bedetermined during the precomputation step described above. Thus, “LiveGames,” “TV schedule,” “web videos,” and “past games” are selected asclusters because they are common types of content items associated withthe broader concept “baseball”. Note, the content item “Blue Jays @ RedSox” 506 of FIG. 5, the content item “Yankees @ Royals” 607 of FIG. 6,and concept cluster “Baseball Web Videos” 606 of FIG. 6 are not includedin the newly formed concept cluster structure presented in FIG. 7. Thisis so because those content items and clusters did not match both inputs“RE” and “YAN”.

The dynamic intersection of concepts is also performed if the user firstentered “RE” and then selected the “Red Sox” concept (as described inconnection with FIG. 5) and then typed “YAN” while in the “Red Sox”concept cluster. Similarly, the user can browse a tree arrangement ofinformation nodes to arrive at a similar result. Thus, the user couldbrowse to a top-level node “Sports”, followed by selection of the childnode “Major League Baseball”, further followed by selection of the “RedSox” node. Once in the “Red Sox” cluster, the user could enter thesearch term “YAN” to complete the dynamic intersection of the conceptclusters “Red Sox” and “New York Yankees”. In the alternate, the usercould indicate through the interface that the “Red Sox” cluster is to bepart of a dynamic intersection query and browse up the tree to find the“New York Yankees” cluster and add that cluster to the intersection.

A system implementing such a search can be configured to enable thistype of search method by maintaining the query state of the user'ssearch session, e.g., the system tracks that the user is currentbrowsing within the “Red Sox” concept. Thus, when the user begins toenter text after having browsed to the concept cluster “Red Sox”, thesystem would use the new text entry along with the current cluster toform the completed query rather than take the new text entry as astandalone query entry. Such a system can also be configurable to nottrack the state of the user, in which case, the new text entry would betreated as a standalone query. Similarly, a device implementing such asystem can provide an “escape” key that would allow the user to resetthe query state, providing the ability to enter a new standalone queryregardless of the user's location in the content hierarchy.

The description above illustrates how the precomputed cluster hierarchycan be flattened and/or merged to form a new hierarchy into whichcontent items are organized for presentation. Concept clusters can alsobe combined to form new, conflated concept clusters, which contain anaggregation of content items that are otherwise organized in differentclusters. For example, FIG. 8 illustrates another possible conceptcluster hierarchy 800 and an example of the formation of adynamically-formed, conflated concept cluster 801. In this hierarchy, aTom Jones cluster 802 is organized under the singers cluster 803.However, there is also a Tom Jones cluster 804 under the actors cluster805 because he has appeared in a movie, there are web videos about him,and some of his concerts have been televised. Thus, when the user entersthe incremental search text “TO JO” 806, the content items under the TomJones singer cluster 802 and Tom Jones actor cluster 804 will bereturned because “TO” incrementally matches “Tom” and “JO” incrementallymatched “Jones”. This is another example of an explicit conceptualcluster. In addition, content items for other personalities matching thesearch text may be returned, such as content items for composer “TomJohnson”, baseball player “Todd Jones”, and other matches. Each of thesepersonalities can have corresponding concept clusters.

In order to assist the user in finding the desired content items, thesystem can organize the content items according to the associatedpersonality concept clusters 807. Thus, the system will dynamicallycreate a general concept cluster for Tom Jones 808 and combine thesub-clusters under the Tom Jones actor cluster 804 and the sub-clustersunder the Tom Jones singer cluster 802 so they are grouped under thedynamically-formed general Tom Jones cluster 808. Thus, the user canfirst select the personality Tom Jones 809 in which he or she isinterested, and then further browse into the specific type of content heor she is seeking 810. The dynamically-formed concept cluster Tom Jones808 can contain sub-clusters as well as content items, e.g., “She's alady”.

FIG. 9 is an illustration of a content system 900 for use with thetechniques described herein. In one implementation, the content system900 has an input device 901 for receiving the user's search input and apresentation device 902 for presenting the selected content items in thedynamically-generated hierarchy. The input device 901 has a keypadand/or navigation interface, described below, to enable the user toenter query input. The presentation device 902 has a presentation screenfor displaying content item search results and the content itself. Theinput and presentation devices 901, 902 could be the same device, as inthe case of, for example, a mobile telephone, a PDA, or any otherhandheld computing device. Such a device may have a full QWERTY keyboardor equivalent, or the device may be an input-constrained device. Inputconstrained devices typically have limited input capabilities comparedto devices having full keyboards. The 12-button keypad of a typicalmobile phone provides one example of an input constrained device. Theinput device 901 and presentation device 902 can also be separatedevices. For example, a television remote control can serve as the inputdevice 901, while the television itself is the presentation device 902.

The system 900 also includes a content provider 903 for maintaining andproviding content to the presentation device 902. The content provider903 has a content catalog 904, a hierarchy catalog 905, and a queryprocessing engine 906. The content catalog 904 contains the contentitems and associated data, such as the metadata terms that describe thevarious content items. The hierarchy catalog 905 contains the variousconcept cluster hierarchies associated with the content items, asdescribed above. The query processing engine 906 receives the user queryinput and selects content items matching the query input (see theincorporated applications for examples of content item selectiontechniques).

The components of the content provider 903 can be present in a singleserver machine, or can be divided among multiple networked machines.Likewise, the various components can be combined or distributed in anumber of ways. For example, the content catalog 904 can also store thehierarchies associated with the content items. In addition, a listing ofthe content items, the associated metadata, and the hierarchyinformation could be stored separately from the content items. Thiswould enable the content list and associated data to be stored on theinput device 901 and/or presentation device 902, while the actualcontent itself would be retained remotely. In some implementations, someor a portion of the content itself can be stored on the input device 901and/or the presentation device 902.

The input device 901 communicates the user input to the content provider903, and the content provider 903 returns the appropriate content itemresults to the presentation device 902, using the techniques describedand incorporated above. The components of system 900 can communicate bya variety of known networking methods, including wired and wirelessmethods.

FIG. 10 illustrates a user device 1000 for use with the techniques andsystems described above. The user device 1000 provides one example of adevice that serves as both the input device 901 and presentation device902 of FIG. 9. The user device 1000 has a keypad 1001 with a full orinput-constrained keypad for text entry and a navigation interface 1002,such as a five-button navigation interface, for enabling the user tobrowse the content items hierarchies, content item results, or contentitems themselves. The user device 1000 also includes a presentation area1003 for displaying content items, hierarchies, and content item resultlists. Presentation area 1003 includes a query display area 1004 fordisplaying the user's query input and a content display area 1005 forpresenting the content items that have been grouped into thedynamically-formed concept clusters. The content display area 1005 canbe further divided into a cluster identification area 1006 fordisplaying the currently selected cluster and a hierarchy display area1007 for displaying content items or sub-clusters grouped under theselected cluster.

Note that the organization of information for browse purposes may differfrom the hierarchy used for the presentation of dynamically-formedconcept clusters. Furthermore, the incremental search input could haveorthographic or typographic errors. The methods described in theincorporated applications can be used to overcome such errors and (1)enable the present methods to match the partial prefix input containingthese errors with results and (2) generate dynamic cluster hierarchies,wherever meaningful.

This form of non-lexical concept-driven clustering of content itemsearch results greatly enhances the user experience on display and/orinput constrained devices such as television, cell phones, and PDA(personal digital assistants) because the user can discover the resultsof interest with minimal effort. However, methods and techniquesdescribed herein can be used with other user interfaces, for example,standard keyboards and/or mouse devices to achieve similar benefits.

It will be appreciated that the scope of the present invention is notlimited to the above-described embodiments, but rather is defined by theappended claims, and these claims will encompass modifications of andimprovements to what has been described. For example, the embodimentsprovided above are described in terms of providing audio/video content.However, the techniques, methods, and systems described and incorporatedherein can be implemented with other content, such as address bookentries, contact information, personal schedule information, or othertypes of data. In addition, a wide variety of physical devices canemploy the techniques disclosed herein, e.g., PDAs, mobile telephones,and handheld PCs. These types of devices share many of the sameconstraints, namely, limited input and/or output capabilities, and thus,can benefit from aspects of the invention provided herein.

1. A method of searching for and presenting content items as anarrangement of conceptual clusters to facilitate further search andnavigation on a display-constrained device, the method comprising:providing a relatively large set of content items, at least some of thecontent items having metadata to specify explicit concepts associatedwith the content items, and at least some of the metadata includingphrases having more than one metadata term; receiving from a userincremental input to incrementally identify more than one search termfor desired content items; selecting from the relatively large set ofcontent items: a first set of content items, wherein all search termsmatch metadata terms of a single one of the metadata phrases of eachcontent item of said first set; a second set of content items, wherein afirst subset of the search terms matches at least one metadata term ofat least a first metadata phrase of each content item of said secondset; and a third set of content items, wherein a second subset of thesearch terms matches at least one metadata term of at least a secondmetadata phrase of each content item of said third set, the firstmetadata phrase differing from the second metadata phrase; grouping thecontent items the second and third sets have in common to form anintersection set for user-implied concepts inferred from the explicitconcepts associated with the metadata of the content items of theintersection set; organizing the content items of the first set and theintersection set into conceptual cluster sets: the content items of thefirst set being organized into explicit conceptual cluster sets based onthe metadata phrases having metadata terms matching the search terms sothat content items having a same metadata phrase matching the searchterms are clustered together; and the content items of the intersectionset being organized into user-implied conceptual clusters based on atleast the first and second metadata phrases the content items of theintersection set have in common so that content items having same firstand second metadata phrases matching the search terms are clusteredtogether; and presenting the content items organized into the explicitconceptual cluster sets and the user-implied conceptual cluster sets:each explicit conceptual cluster set identified based on the metadataphrase common to the content items of said explicit conceptual clusterset having metadata terms matching the search terms; and eachuser-implied conceptual cluster set identified based on the first andsecond metadata phases the content items of said user-implied conceptualcluster set have in common.
 2. The method of claim 1, wherein theincremental input is ambiguous text input, the ambiguous text inputhaving one or more digits, each digit representing more than onealphanumeric character.
 3. The method of claim 1, further comprisingmodifying the metadata terms of at least one of the metadata phrases ofat least some of the content items based on at least one of the date,day, and time of the incremental input.
 4. The method of claim 1,wherein the presenting the content items is on a display-constraineddevice.
 5. The method of claim 1, wherein the incremental inputcomprises at least two prefixes in an ordered format.
 6. The method ofclaim 1, wherein the incremental input comprises at least two prefixesin an unordered format.
 7. The method of claim 1, wherein theincremental input comprises at least two prefixes separated by a wordseparator.
 8. The method of claim 1, wherein the organized content itemsare ordered for presentation in accordance with a given relevancefunction, the relevance function comprising at least one of temporalrelevance of the content items, location relevance of the content items,popularity of the content items, and preferences of the user.
 9. Themethod of claim 1, wherein at least some of the metadata terms includephonetically equivalent terms to the explicit concepts associated withat least some of the content items.
 10. The method of claim 1, whereinat least some of the metadata terms include commonly misspelled terms ofthe terms of the metadata phrases.
 11. The method of claim 1, furthercomprising organizing the content items of the large set of contentitems into a predetermined hierarchy based on a relationship between theinformational content of the content items, wherein the metadata tospecify the explicit concepts associated with the content items isselected based on the predetermined hierarchy.
 12. A system forsearching for and presenting content items as an arrangement ofconceptual clusters to facilitate further search and navigation on adisplay-constrained device, the system comprising: a database stored inan electronically readable medium for cataloging a relatively large setof content items, at least some of the content items having metadata tospecify explicit concepts associated with the content items, and atleast some of the metadata including phrases having more than onemetadata term; input logic for receiving from a user incremental inputto incrementally identify more than one search term for desired contentitems; selection logic for selecting from the relatively large set ofcontent items: a first set of content items, wherein all search termsmatch metadata terms of a single one of the metadata phrases of eachcontent item of said first set; a second set of content items, wherein afirst subset of the search terms matches at least one metadata term ofat least a first metadata phrase of each content item of said secondset; and a third set of content items, wherein a second subset of thesearch terms matches at least one metadata term of at least a secondmetadata phrase of each content item of said third set, the firstmetadata phrase differing from the second metadata phrase; groupinglogic for grouping the content items the second and third sets have incommon to form an intersection set for user-implied concepts inferredfrom the explicit concepts associated with the metadata of the contentitems of the intersection set; organization logic for organizing thecontent items of the first set and the intersection set into conceptualcluster sets: the content items of the first set being organized intoexplicit conceptual cluster sets based on the metadata phrases havingmetadata terms matching the search terms so that content items having asame metadata phrase matching the search terms are clustered together;and the content items of the intersection set being organized intouser-implied conceptual clusters based on at least the first and secondmetadata phrases the content items of the intersection set have incommon so that content items having same first and second metadataphrases matching the search terms are clustered together; andpresentation logic for presenting the content items organized into theexplicit conceptual cluster sets and the user-implied conceptual clustersets: each explicit conceptual cluster set identified based on themetadata phrase common to the content items of said explicit conceptualcluster set having metadata terms matching the search terms; and eachuser-implied conceptual cluster set identified based on the first andsecond metadata phases the content items of said user-implied conceptualcluster set have in common.
 13. The system of claim 12, wherein at leasta portion of the database stored in an electronically readable medium isimplemented in a server system remote from the user.
 14. The system ofclaim 12, wherein at least one of the input logic, the selection logic,the grouping logic, the organization logic, and the presentation logicis implemented in a server system remote from the user.
 15. The systemof claim 12, wherein the incremental input is ambiguous text input, theambiguous text input having one or more digits, each digit representingmore than one alphanumeric character.
 16. The system of claim 12,further comprising modification logic for modifying the metadata termsof at least one of the metadata phrases of at least some of the contentitems based on at least one of the date, day, and time of theincremental input.
 17. The system of claim 12, further comprisingranking logic for ordering the organized content items for presentationin accordance with a given relevance function, wherein the relevancefunction comprises at least one of temporal relevance of the contentitems, location relevance of the content items, popularity of thecontent items, and preferences of the user.